Multiple Imputation and its Application
The most up-to-date edition of a bestselling guide to analyzing partially observed data
In this comprehensively revised Second Edition of
Multiple Imputation and its Application,
a team of distinguished statisticians delivers an overview of the issues raised by missing data, the rationale for multiple imputation as a solution, and the practicalities of applying it in a multitude of settings.
With an accessible and carefully structured presentation aimed at quantitative researchers,
is illustrated with a range of examples and offers key mathematical details. The book includes a wide range of theoretical and computer-based exercises, tested in the classroom, which are especially useful for users of R or Stata. Readers will find:
A comprehensive overview of one of the most effective and popular methodologies for dealing with incomplete data sets
Careful discussion of key concepts
A range of examples illustrating the key ideas
Practical advice on using multiple imputation
Exercises and examples designed for use in the classroom and/or private study
Written for applied researchers looking to use multiple imputation with confidence, and for methods researchers seeking an accessible overview of the topic,
will also earn a place in the libraries of graduate students undertaking quantitative analyses.